The increased number of cancer cases reported in the United States, and, indeed, around the world, is a major concern. Currently there are only a handful of treatments available for specific types of cancer, and these provide no guarantee of success. In order to be most effective, these treatments require not only an early detection of the malignancy, but a reliable assessment of the severity of the malignancy.
The incidence of breast cancer, a leading cause of death in women, has been gradually increasing in the United States over the last thirty years. Its cumulative risk is relatively high; 1 in 8 women are expected to develop some type of breast cancer by age 85 in the United States. In fact, breast cancer is the most common cancer in women and the second most common cause of cancer death in the United States. In 1997, it was estimated that 181,000 new cases were reported in the U.S., and that 44,000 people would die of breast cancer (Parker et al., CA Cancer J. Clin. 47:5-27 (1997); Chu et al., J. Nat. Cancer Inst. 88:1571-1579 (1996)). While mechanism of tumorigenesis for most breast carcinomas is largely unknown, there are genetic factors that can predispose some women to developing breast cancer (Miki et al., Science, 266:66-71 (1994)).
Sporadic tumors, those not currently associated with a known germline mutation, constitute the majority of breast cancers. It is also likely that other, non-genetic factors also have a significant effect on the etiology of the disease. Regardless of the cancer's origin, breast cancer morbidity and mortality increases significantly if it is not detected early in its progression. Thus, considerable effort has focused on the early detection of cellular transformation and tumor formation in breast tissue.
A marker-based approach to tumor identification and characterization promises improved diagnostic and prognostic reliability. Typically, the diagnosis of breast cancer requires histopathological proof of the presence of the tumor. In addition to diagnosis, histopathological examinations also provide information about prognosis and selection of treatment regimens. Prognosis may also be established based upon clinical parameters such as tumor size, tumor grade, the age of the patient, and lymph node metastasis.
Diagnosis and/or prognosis may be determined to varying degrees of effectiveness by direct examination of the outside of the breast, or through mammography or other X-ray imaging methods (Jatoi, Am. J. Surg. 177:518-524 (1999)). The latter approach is not without considerable cost, however. Every time a mammogram is taken, the patient incurs a small risk of having a breast tumor induced by the ionizing properties of the radiation used during the test. In addition, the process is expensive and the subjective interpretations of a technician can lead to imprecision. For example, one study showed major clinical disagreements for about one-third of a set of mammograms that were interpreted individually by a surveyed group of radiologists. Moreover, many women find that undergoing a mammogram is a painful experience. Accordingly, the National Cancer Institute has not recommended mammograms for women under fifty years of age, since this group is not as likely to develop breast cancers as are older women. It is compelling to note, however, that while only about 22% of breast cancers occur in women under fifty, data suggests that breast cancer is more aggressive in pre-menopausal women.
In clinical practice, accurate diagnosis of various subtypes of breast cancer is important because treatment options, prognosis, and the likelihood of therapeutic response all vary broadly depending on the diagnosis. Accurate prognosis, or determination of distant metastasis-free survival could allow the oncologist to tailor the administration of adjuvant chemotherapy, with women having poorer prognoses being given the most aggressive treatment. Furthermore, accurate prediction of poor prognosis would greatly impact clinical trials for new breast cancer therapies, because potential study patients could then be stratified according to prognosis. Trials could then be limited to patients having poor prognosis, in turn making it easier to discern if an experimental therapy is efficacious.
To date, no set of satisfactory predictors for prognosis based on the clinical information alone has been identified. Many have observed that the ER status has a dominant signature in the breast tumor gene expression profiling. See West et al., Proc. Natl. Acad. Sci. U.S.A. 98:11462 (2001); van 't Veer et al., Nature 415:530 (2002); Sorlie et al., Proc. Natl. Acad. Sci. U.S.A. 100:8418 (2003); Perou et al. Nature 406:747 (2000); Gruvberger et al., Cancer Res. 61:5979 (2001); Sotiriou et al., Proc. Natl. Acad. Sci. U.S.A. 100:10393 (2003). It is generally accepted that there is some relationship between patient survival and ER status. van de Vijver et al., N. Engl. J. Med. 347:1999 (2002); Surowiak et al, Folia Histochem. Cytobiol. 39:143 (2001); Pichon et al. Br. J Cancer 73:1545 (1996); Collett et al., J. Clin. Pathol. 49:920 (1996). BRCA1 mutations are related to the familial cancer susceptibility. Biesecker et al., JAMA 269:1970 (1993); Easton et al., Cancer Surv. 18:95 (1993). Age is also considered to be a prognosis factor since young cancer patients tend to have poor tumors. Maggard et al., J. Surg. Res. 113:109 (2003). Lymph node status is a factor in deciding the treatment. Eifel et al., J. Natl. Cancer Inst. 93:979 (2001).
The discovery and characterization of BRCA1 and BRCA2 has recently expanded our knowledge of genetic factors which can contribute to familial breast cancer. Germ-line mutations within these two loci are associated with a 50 to 85% lifetime risk of breast and/or ovarian cancer (Casey, Curr. Opin. Oncol. 9:88-93 (1997); Marcus et al., Cancer 77:697-709 (1996)). Only about 5% to 10% of breast cancers, however, are associated with breast cancer susceptibility genes, BRCA1 and BRCA2. The cumulative lifetime risk of breast cancer for women who carry the mutant BRCA1 is predicted to be approximately 92%, while the cumulative lifetime risk for the non-carrier majority is estimated to be approximately 10%. BRCA1 is a tumor suppressor gene that is involved in DNA repair and cell cycle control, which are both important for the maintenance of genomic stability. More than 90% of all mutations reported so far result in a premature truncation of the protein product with abnormal or abolished function. The histology of breast cancer in BRCA1 mutation carriers differs from that in sporadic cases, but mutation analysis is the only way to find the carrier. Like BRCA1, BRCA2 is involved in the development of breast cancer, and like BRCA1 plays a role in DNA repair. However, unlike BRCA1, it is not involved in ovarian cancer.
Other genes have been linked to breast cancer, for example c-erb-2 (HER2) and p53 (Beenken et al., Ann. Surg. 233(5):630-638 (2001). Overexpression of c-erb-2 (HER2) and p53 have been correlated with poor prognosis (Rudolph et al., Hum. Pathol. 32(3):311-319 (2001), as has been aberrant expression products of mdnm2 (Lukas et al., Cancer Res. 61(7):3212-3219 (2001) and cyclin1 and p27 (Porter & Roberts, International Publication WO98/33450, published Aug. 6, 1998).
The detection of BRCA1 or BRCA2 mutations represents a step towards the design of therapies to better control and prevent the appearance of these tumors. Recently, many studies have used gene expression profiling to analyze various cancers, and those studies have provided new diagnosis and prognosis information in the molecular level. See Zajchowski et al., “Identification of Gene Expression Profiled that Predict the Aggressive Behavior of Breast Cancer Cells,” Cancer Res. 61:5168 (2001); West et al., “Predicting the Clinical Status of Human Breast Cancer by Using Gene Expression Profiles,” Proc. Natl. Acad. Sci. U.S.A. 98:11462 (2001); van 't Veer et al., “Gene Expression Profiling Predicts the Outcome of Breast Cancer,” Nature 415:530 (2002); Roberts et al., “Diagnosis and Prognosis of Breast Cancer Patients,” WO 02/103320; Sorlie et al., Proc. Natl. Acad. Sci. U.S.A. 100:8418 (2003); Perou et al., Nature 406:747 (2000); Khan et al., Cancer Res 58, 5009 (1998); Golub et al., Science 286, 531 (1999); DeRisi et al., Nat. Genet. 14:457 (1996); Alizadeh et al., Nature 403, 503 (2000). Methods for the identification of informative genesets for various cancers have also been described. See Roberts et al., “Diagnosis and Prognosis of Breast Cancer Patients,” WO 02/103320; Golub et al., U.S. Pat. No. 6,647,341.
Genesets have been identified that are informative for differentiating individuals having, or suspected of having, breast cancer based on estrogen receptor (ER) status, or BRCA1 mutation vs. sporadic (i.e., other than BRCA1-type) mutation status. See Roberts et al., WO 02/103320; van't Veer et al., Nature 415:530 (2001). Genesets have also been identified that enable the classification of sporadic tumor-type individuals as those who will likely have no metastases within five years of initial diagnosis (i.e., individuals with a good prognosis) or those who will likely have a metastasis within five years of initial diagnosis (i.e., those having a poor prognosis). Roberts, supra; van't Veer, supra.
Roberts et al. WO 02/103320 describes a 70-gene set, useful for the prognosis of breast cancer, which outperformed clinical measures of prognosis, and which showed good potential in selecting good outcome patients, thereby avoiding over-treatment. van de Vijver et al., N. Engl. J. Med. 347:1999 (2002). The expression of genes with most predictive value, however, were not homogeneous among poor patients, suggesting the need for improvement.
Although the patterns of gene expression as described in Roberts et al. were correlated with existing clinical indicators such as estrogen receptor and BRCA1 status, clinical measures were not incorporated. Furthermore, although the poor-outcome group in particular showed heterogeneity in expression pattern, the best classifier decision rule found during these studies was a fairly simple one based on the similarity of a patient profile to the average profile of a good-outcome training group.
It is evident that breast cancer is the result of more than one type of molecular event. Likewise, a variety of other conditions, such as other cancers; non-cancer diseases such as diabetes, autoimmune or neurodegenerative disorders, obesity; etc., are also the result of more than one molecular event. Moreover, an individual's response to exposure to particular environmental conditions, for example, exposure to natural or man-made agents, such as toxins, pollutants, drugs, food additives, etc., likely result from more than one molecular event. Thus, there exists a need for improved prognostic methods so that appropriate courses of prophylaxis and/or therapy may be provided. Genesets having improved prognostic power can be identified by first identifying discrete subsets of individuals based on genotypic or phenotypic characteristics relevant to the disease or condition, and then identifying genesets informative for prognosis within those subsets of patients. Individuals having the condition, or who are suspected of having the condition, such as breast cancer, would then be provided therapies appropriate to the molecular mechanisms underlying the condition. The present invention provides such methods for breast cancer, and for other cancers, diseases or conditions.